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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 19 days ago
was installed at IT4Innovations in 2025. For more details, see www.it4i.eu . Activity description: · modelling and optimization of electrical networks using open source tools (preferably Julia or Python
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Computer Engineering. Expertise in computer vision algorithms and image processing techniques (such as object detection, segmentation, and feature extraction). Proficiency in deep learning frameworks such as
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. Experience with technical software such in the fields of design, construction and/or building operations. Including programs such as: Dropbox, Microsoft Office Suite, 3D Modeling, and willingness to learn new
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/interventions, and clinical diagnoses. The post would be suitable for applicants with general interests in AI, machine learning, large language models, foundation models, signal processing, computational
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discipline. Experience with deep learning framework PyTorch or similar. Strong background in machine learning, image or signal processing. Knowledge of SotA models for multi-modality and scene understanding
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& Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame
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of 3D crystalline structures; – depending on the candidate's profile, implementing machine learning methods (AI & machine learning) for the analysis of physicochemical data from the hpmat.org database
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insurance, generous paid leave and retirement programs. To learn more about UofSC benefits, access the "Working at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Research Grant or Time
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models, and ensuring students have a structured and engaging learning experience. Career Readiness Competencies: Access & Opportunity Leadership Professionalism Essential Functions Teach assigned courses
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. The positions focus on applied machine learning methods for real-world systems. Possible research directions include: Transfer learning and domain adaptation across heterogeneous production environments (e.g